ImageProc
bhaskar
How to convert matrix of values into greyscale image in R ?
vec<-as.vector(rnorm(1:924))
heatmap(matrix(vec,33,28),Rowv=NA,Colv=NA,col=paste("gray",1:99,sep=""))
image(matrix(vec,33,28),col = paste("gray",1:99,sep=""),Rowv = NA,Colv=NA)## Warning in plot.window(...): "Rowv" is not a graphical parameter## Warning in plot.window(...): "Colv" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "Rowv" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "Colv" is not a graphical parameter## Warning in axis(side = side, at = at, labels = labels, ...): "Rowv" is not
## a graphical parameter## Warning in axis(side = side, at = at, labels = labels, ...): "Colv" is not
## a graphical parameter## Warning in axis(side = side, at = at, labels = labels, ...): "Rowv" is not
## a graphical parameter## Warning in axis(side = side, at = at, labels = labels, ...): "Colv" is not
## a graphical parameter## Warning in box(...): "Rowv" is not a graphical parameter## Warning in box(...): "Colv" is not a graphical parameter## Warning in title(...): "Rowv" is not a graphical parameter## Warning in title(...): "Colv" is not a graphical parameter
# different way to use full range of grays
grays = rgb(red = 0:255/255, blue = 0:255/255, green = 0:255/255)
heatmap(matrix(rnorm(100), 10, 10),Rowv=NA,Colv=NA,col=grays, scale = "none")
using package X for basic processing
#Install package
#source("http://bioconductor.org/biocLite.R")
#biocLite()
#biocLite("EBImage")
# Load the librray to current R session
library("EBImage")
# Read image file
Image=readImage("amyjackson.jpg")
display(Image)
# display command open the image in web-browser.
# get details of image file like pixel value, colormode etc
print(Image)## Image
## colorMode : Color
## storage.mode : double
## dim : 736 1059 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9529412 0.9529412 0.9529412 0.9529412 0.9607843 0.9607843
## [2,] 0.9647059 0.9647059 0.9647059 0.9647059 0.9647059 0.9725490
## [3,] 0.9764706 0.9764706 0.9764706 0.9764706 0.9764706 0.9803922
## [4,] 0.9843137 0.9843137 0.9843137 0.9843137 0.9843137 0.9882353
## [5,] 0.9882353 0.9882353 0.9882353 0.9960784 0.9960784 0.9960784# Adjust brightness
Image1=Image+0.4
Image2=Image-0.4
display(Image1)
display(Image2)
#to write image file
writeImage(Image1, "Image1.jpg")
#Adjusting Contrast
Image3=Image*0.7
Image4=Image*1.5
display(Image3)
display(Image4)
writeImage(Image4, "Image4.jpg")
# gamma correction
Image5=Image^2
Image6=Image^0.5
display(Image5)
display(Image6)
print(Image)## Image
## colorMode : Color
## storage.mode : double
## dim : 736 1059 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9529412 0.9529412 0.9529412 0.9529412 0.9607843 0.9607843
## [2,] 0.9647059 0.9647059 0.9647059 0.9647059 0.9647059 0.9725490
## [3,] 0.9764706 0.9764706 0.9764706 0.9764706 0.9764706 0.9803922
## [4,] 0.9843137 0.9843137 0.9843137 0.9843137 0.9843137 0.9882353
## [5,] 0.9882353 0.9882353 0.9882353 0.9960784 0.9960784 0.9960784#Cropping
Image7=Image[200:600,100:300,]
display(Image7)
writeImage(Image7, "Image7.jpg")
#Spatial Transformation
?translate
print(Image7)## Image
## colorMode : Color
## storage.mode : double
## dim : 401 201 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.003921569 0.003921569 0.003921569 0.003921569 0.003921569
## [2,] 0.003921569 0.003921569 0.003921569 0.003921569 0.003921569
## [3,] 0.003921569 0.003921569 0.003921569 0.003921569 0.003921569
## [4,] 0.003921569 0.003921569 0.003921569 0.003921569 0.003921569
## [5,] 0.003921569 0.003921569 0.003921569 0.003921569 0.003921569
## [,6]
## [1,] 0.003921569
## [2,] 0.003921569
## [3,] 0.003921569
## [4,] 0.003921569
## [5,] 0.003921569Image8=translate(rotate(Image7,45),c(100,100))
image9 = translate(rotate(Image7,60),c(100,100))
display(Image8)
print(Image8)## Image
## colorMode : Color
## storage.mode : double
## dim : 426 426 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0print(image9)## Image
## colorMode : Color
## storage.mode : double
## dim : 375 448 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0writeImage(Image8, "Image8.jpg")
#Color Management
## while converting from RGB to gray, three component images result corresponding to R, G and B respectively.
?colormode
x = Image(rnorm(300*300*3),dim=c(300,300,3), colormode='Color')
display(x)
colorMode(Image)## [1] 2colorMode(Image)=Grayscale
print(Image)## Image
## colorMode : Grayscale
## storage.mode : double
## dim : 736 1059 3
## frames.total : 3
## frames.render: 3
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9529412 0.9529412 0.9529412 0.9529412 0.9607843 0.9607843
## [2,] 0.9647059 0.9647059 0.9647059 0.9647059 0.9647059 0.9725490
## [3,] 0.9764706 0.9764706 0.9764706 0.9764706 0.9764706 0.9803922
## [4,] 0.9843137 0.9843137 0.9843137 0.9843137 0.9843137 0.9882353
## [5,] 0.9882353 0.9882353 0.9882353 0.9960784 0.9960784 0.9960784display(Image)## Only the first frame of the image stack is displayed.
## To display all frames use 'all = TRUE'.
writeImage(Image, "Image_gray.jpg")
#revert the color change
colorMode(Image)=Color
display(Image)
#Combine Images .... here combining images means having several frames in one image. no of frames
# being equal to number of images being combined.
print(Image8)## Image
## colorMode : Color
## storage.mode : double
## dim : 426 426 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0# Imagecomb=combine( Image9, Image8)
#
# display(Imagecomb)
# Morph Image....
## Image morph can only be done for binary image.
## A binary image is the one which have only two pixel value, 0 for background and 1 for object.
Image9=readImage("binaryimage.png")
print(Image9)## Image
## colorMode : Grayscale
## storage.mode : double
## dim : 200 140
## frames.total : 1
## frames.render: 1
##
## imageData(object)[1:5,1:6]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 0 0
## [2,] 1 0 0 0 0 0
## [3,] 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0?makeBrush
kern=makeBrush(5, shape='diamond')
Imageerode=erode(Image9, kern)
Imagedilat=dilate(Image9, kern)
display(Imageerode)
display(Imagedilat)
writeImage(Imageerode, "Imageerode.jpg")
writeImage(Imagedilat, "Imagedilat.jpg")
#Segmentation
## for a binary image, segmentation counts no of objects.
Imagelabel=bwlabel(Image9)
cat('Number of objects=', max(Imagelabel),'\n')## Number of objects= 3081##output of above line, 'Number of objects=3081'
# Filteration
## low pass filter
## low pass filter can remove noise or blur images
flow=makeBrush(19, shape='diamond', step=FALSE)^2
flow=flow/sum(flow)
imageflow=filter2(Image, flow)
display(imageflow)
## High pass filter
## high pass filter can detact edges or make images sharper.
fhi=matrix(1, nc=3, nr=3)
fhi[2,2]=-8
imagefhi=filter2(Image, fhi)
display(imagefhi)
writeImage(imagefhi,'highpassfilterImage.jpg')package *** imager*** is another alternative useful package to explore, as of now still undecided on which package to use.
KMeans clustering on image
library(jpeg)
#GET IMAGE
#url <- "http://www.wall321.com/thumbnails/detail/20120304/colorful%20birds%20tropical%20head%203888x2558%20wallpaper_www.wall321.com_40.jpg"
# Download the file and save it as "Image.jpg" in the directory
#dFile <- download.file(url, "Image.jpg")
img <- readJPEG("kmeansImage.jpg") #
imgDm <- dim(img)
#CLEAN
# Assign RGB channels to data frame
imgRGB <- data.frame(
x = rep(1:imgDm[2], each = imgDm[1]),
y = rep(imgDm[1]:1, imgDm[2]),
R = as.vector(img[,,1]),
G = as.vector(img[,,2]),
B = as.vector(img[,,3])
)
#PLOTTING
library(ggplot2)
# ggplot theme to be used
plotTheme <- function() {
theme(
panel.background = element_rect(
size = 3,
colour = "black",
fill = "white"),
axis.ticks = element_line(
size = 2),
panel.grid.major = element_line(
colour = "gray80",
linetype = "dotted"),
panel.grid.minor = element_line(
colour = "gray90",
linetype = "dashed"),
axis.title.x = element_text(
size = rel(1.2),
face = "bold"),
axis.title.y = element_text(
size = rel(1.2),
face = "bold"),
plot.title = element_text(
size = 20,
face = "bold",
vjust = 1.5)
)
}
# Plot the image
ggplot(data = imgRGB, aes(x = x, y = y)) +
geom_point(colour = rgb(imgRGB[c("R", "G", "B")])) +
labs(title = "Original Image: Colorful Bird") +
xlab("x") +
ylab("y") +
plotTheme()
#CLUSTERING
kClusters <- 3
kMeans <- kmeans(imgRGB[, c("R", "G", "B")], centers = kClusters)
kColours <- rgb(kMeans$centers[kMeans$cluster,])
#PLOT KMEANS
ggplot(data = imgRGB, aes(x = x, y = y)) +
geom_point(colour = kColours) +
labs(title = paste("k-Means Clustering of", kClusters, "Colours")) +
xlab("x") +
ylab("y") +
plotTheme()